Financial Risk in Supply Chains: Predicting Bankruptcy in Private Firms Using Public Data
Bibliographic record
Abstract
Risk management plays a critical role in designing and operating effective and resilient supply chains. This paper focuses on bankruptcy as a measure for assessing the financial risk of companies within supply chain networks. While numerous bankruptcy models for public companies exist in literature, there is a lack of predictive bankruptcy models tailored for private firms, which serve as key entities within many supply chain networks. Existing models for private firms either depend on data that would require insider knowledge or focus on countries where private firms must disclose financials publicly. It is notably difficult to predict bankruptcy of private firms in the United States and Canada where such companies are not required by law to publicly disclose financials. This paper introduces an innovative quantitative bankruptcy prediction model tailored for private U.S. companies, leveraging publicly available information including but not limited to sentiment analysis, geographic location, firm age, and economic indicators. The methodology integrates the data of these diverse sources through a logistic regression model which outputs a bankruptcy classification that can be subsequently utilized in the design and planning of resilient supply chains. Our framework provides purchasers and investors with a simple way to assess bankruptcy risk using only publicly available information. The model can also be used in conjunction with other predictive metrics in a holistic risk assessment model.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".